硬件优化,在GOLOMB大米编码中的数据压缩过程中有效降低开关功率
R Sakthivel1, Ch Vijayalakshmi2, M Vanitha3
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
PloS one
|September 26, 2024
概括
这项研究增强了Golomb Rice (GR) 编码以实现无损数据压缩,提高了VLSI测试和AI计算中的硬件效率. 优化的GR代码可以降低比特使用量和功耗,同时保持高压缩比.
科学领域:
- 进行VLSI设计和测试.
- 数据压缩算法数据压缩算法
- 对AI/ML进行硬件加速.
背景情况:
- 无损数据压缩对于VLSI测试矢量生成,硬件AI/ML计算和高效的数据处理至关重要.
- 戈洛姆代码提供了有效的无损压缩,但仅限于分数是二的强.
- 哥伦布大米 (GR) 编码是一种适用于几何分布数据的变体.
研究的目的:
- 通过优化其单元部分的编码来增强Golomb Rice (GR) 编码的压缩比.
- 为了减少GR代码编码的比特使用量和硬件资源需求.
- 在压缩,延迟,面积和功耗方面评估性能改进.
主要方法:
- 开发并实施了一种算法,以进一步编码Golomb Rice代码的单元部分.
- 在Verilog中编码了算法,并使用Modelsim模拟了它的功能.
- 合成了使用Cadence Encounter RTL合成器进行硬件实现分析的Verilog代码.
主要成果:
- 在线分布式数据集中使用的比特减少了6%至19%.
- 最糟糕的情况延迟减少了3%至8%.
- 已证明面积减少范围为22%至36%,切换功耗减少近7%.
结论:
- 拟议的优化大大提高了Golomb Rice编码在硬件实现中的效率.
- 增强的GR编码技术适用于VLSI测试矢量压缩和数据计算,特别是用于几何分布式数据.
- 这些优化有助于更高效的AI/ML计算和数据处理硬件.
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